library(knitr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(plotly)
library(DT)
library(ggthemes)
library(PMCMR)
library(scmamp)
knitr::opts_chunk$set(dpi=96, results = "asis")
knitr::knit_hooks$set(inline = function(x) {
prettyNum(x, big.mark=",")
})
SAVE_EPS = F
prettyTable <- function(table_df, round_columns=numeric(), round_digits=3, make_plot=T) {
DT::datatable(table_df, style="bootstrap", filter = "top", rownames = FALSE, extensions = "Buttons",
options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print'))) %>%
formatRound(round_columns, round_digits)
}
reportFriedman <- function(df, classifier, metric, metric_function, metric_direction=1, make_plot = T){
averages = data.frame(df) %>%
select(-c(Attributes, Number.of.classes, Min.class.examples, Max.class.examples)) %>%
group_by(Dataset, Classifier, Feature.selector) %>%
summarise_all(metric_function, na.rm=T) %>%
data.frame() %>%
filter(Feature.selector != 'All', Classifier == classifier) %>%
select_("Dataset", "Feature.selector", metric) %>%
spread("Feature.selector", metric) %>%
select(-Dataset) %>%
select(`FRFE`, `RFE-log`, `3-SRFE`, `RFE-log-3`, `5-SRFE`, `RFE-log-5`, `10-SRFE`, `RFE-log-10`) %>%
data.matrix()
if (metric_direction == 1) {
averages_r = -averages
averages_t = averages
} else {
averages_r = averages
averages_t = -averages
}
ranks <- t(apply(averages_r, 1, rank))
ranks <- ranks[,order(colMeans(ranks, na.rm=TRUE))]
cat("<hr><strong>Friedman rank sum test</strong><br />")
fTest <- friedman.test(averages_t)
testResult <- capture.output(print(fTest))
cat(testResult[5])
cat("\r\n")
mean_ranks <- t(colMeans(ranks, na.rm=TRUE))
print(kable(mean_ranks, digits = 2))
cat("\r\n")
w_df <- data.frame(Subsecting = c("FRFE", "3-SRFE", "5-SRFE", "10-SRFE"),
Standard = c("RFE-log", "RFE-log-3", "RFE-log-5", "RFE-log-10"),
p.value = c(-1, -1,-1, -1)) %>%
mutate(Hypothesis = paste0(Subsecting, " vs ", Standard))
cat("<hr><strong>Wilcoxon signed rank test</strong><br />")
for (r in 1:nrow(w_df)){
wTest <- wilcox.test(averages_t[,as.character(w_df[r, "Subsecting"])],
averages_t[,as.character(w_df[r, "Standard"])],
paired=TRUE, alternative = "g")
w_df[r, "p.value"] <- wTest$p.value
}
print(kable(w_df %>% select(Hypothesis, p.value), digits = 3))
cat("\r\n")
if (make_plot){
setEPS()
par(mar=c(0,0,0,0))
postscript(paste0("images/", metric, "_", classifier, "_", "Friedman.eps"), width = 7, height = 3.4)
plotCD(results.matrix = averages_t, alpha = 0.05, cex = 1.1)
dev.off()
}
cat("<hr>")
mean_ranks_df <- data.frame(`FRFE` = mean_ranks[1, "FRFE"], `RFE-log` = mean_ranks[1, "RFE-log"], `3-SRFE` = mean_ranks[1, "3-SRFE"], `RFE-log-3` = mean_ranks[1, "RFE-log-3"], `5-SRFE` = mean_ranks[1, "5-SRFE"], `RFE-log-5` = mean_ranks[1, "RFE-log-5"], `10-SRFE` = mean_ranks[1, "10-SRFE"], `RFE-log-10` = mean_ranks[1, "RFE-log-10"], `p-value` = fTest$p.value)
mean_ranks_df
}
Results summary
Raw results
df <- read.csv("Benchmarks.csv", na.strings = c("?", "", "-")) %>%
mutate(Dataset = as.character(Dataset), Classifier = as.character(Classifier), Selected.num = as.character(Selected.num)) %>%
select(-c(Start.date, Selector.params, Scorer, Grid.scores, Selected.features))
df$Dataset <- substr(df$Dataset, 1, nchar(df$Dataset)-4)
df$Dataset <- as.factor(df$Dataset)
df$Classifier[startsWith(df$Classifier, "SVC")] <- "SVM"
df$Classifier[startsWith(df$Classifier, "Random")] <- "RF"
df$Classifier[startsWith(df$Classifier, "Logistic")] <- "LR"
df$Classifier[startsWith(df$Classifier, "LGBM")] <- "GBM"
df$Classifier <- as.factor(df$Classifier)
df$Selected.num[df$Selected.num == "error"] <- NA
df$Selected.num <- as.numeric(df$Selected.num)
prettyTable(df, c(9, 11:15))
Datasets
datasets_df <- df %>%
select(Dataset, Examples, Attributes, Number.of.classes, Min.class.examples, Max.class.examples) %>%
distinct()
prettyTable(datasets_df)
Mean cross-validation scores
cv_df <- df %>%
select(-c(Attributes, Number.of.classes, Min.class.examples, Max.class.examples)) %>%
group_by(Dataset, Classifier, Feature.selector) %>%
summarise_all(mean, na_rm=T)
prettyTable(cv_df, c(5, 8:12))
Comparisons and statistical tests
classifiers <- as.character(unique(cv_df$Classifier))
metrics <- c("Selected.num", "Selected.num", "Accuracy", "Kappa", "Macro.recall", "G.mean", "Processing.time")
metric_functions <- c(mean, sd, mean, mean, mean, mean, mean)
mertric_headers <-c("Number of selected features", "Standard deviation of number of selected features", "Accuracy", "Kappa", "Macro recall", "G-mean", "Processing time")
metric_direction <- c(-1, -1, 1, 1, 1, 1, -1)
friedman_df <- data.frame(Metric = character(), Classifier = character(), `FRFE` = numeric(), `RFE-log` = numeric(), `3-SRFE` = numeric(), `RFE-log-3` = numeric(), `5-SRFE` = numeric(), `RFE-log-5` = numeric(), `10-SRFE` = numeric(), `RFE-log-10` = numeric(), `p-value` = numeric())
for (i in seq_along(metrics)){
cat(paste0("## ", mertric_headers[i],"\r\n\r\n"))
for (classifier in classifiers){
cat(paste0("### ", classifier,"\r\n\r\n"))
mean_ranks <- reportFriedman(df, classifier, metrics[i], metric_functions[i], metric_direction[i], make_plot = F)
mean_df <- data.frame(Metric = mertric_headers[i], Classifier = classifier, `FRFE` = mean_ranks$FRFE, `RFE-log` = mean_ranks$RFE.log, `3-SRFE` = mean_ranks$X3.SRFE, `RFE-log-3` = mean_ranks$RFE.log.3, `5-SRFE` = mean_ranks$X5.SRFE, `RFE-log-5` = mean_ranks$RFE.log.5, `10-SRFE` = mean_ranks$X10.SRFE, `RFE-log-10` = mean_ranks$RFE.log.10, `p-value` = mean_ranks$p.value)
friedman_df <- rbind(friedman_df, mean_df)
}
}
Number of selected features
GBM
Friedman rank sum testFriedman chi-squared = 43.604, df = 7, p-value = 2.549e-07
| 3.02 |
3.7 |
3.75 |
3.82 |
5.25 |
5.29 |
5.54 |
5.64 |
Wilcoxon signed rank test
| FRFE vs RFE-log |
0.003 |
| 3-SRFE vs RFE-log-3 |
0.000 |
| 5-SRFE vs RFE-log-5 |
0.002 |
| 10-SRFE vs RFE-log-10 |
0.003 |
LR
Friedman rank sum testFriedman chi-squared = 49.037, df = 7, p-value = 2.231e-08
| 2.39 |
3.61 |
3.96 |
4.3 |
5.14 |
5.32 |
5.36 |
5.91 |
Wilcoxon signed rank test
| FRFE vs RFE-log |
0.048 |
| 3-SRFE vs RFE-log-3 |
0.011 |
| 5-SRFE vs RFE-log-5 |
0.023 |
| 10-SRFE vs RFE-log-10 |
0.002 |
RF
Friedman rank sum testFriedman chi-squared = 30.123, df = 7, p-value = 9.013e-05
| 3.29 |
3.43 |
3.71 |
4.46 |
4.96 |
5.18 |
5.39 |
5.57 |
Wilcoxon signed rank test
| FRFE vs RFE-log |
0.866 |
| 3-SRFE vs RFE-log-3 |
0.007 |
| 5-SRFE vs RFE-log-5 |
0.045 |
| 10-SRFE vs RFE-log-10 |
0.008 |
SVM
Friedman rank sum testFriedman chi-squared = 37.86, df = 7, p-value = 3.221e-06
| 3.2 |
3.21 |
4 |
4.41 |
5.04 |
5.11 |
5.18 |
5.86 |
Wilcoxon signed rank test
| FRFE vs RFE-log |
0.023 |
| 3-SRFE vs RFE-log-3 |
0.010 |
| 5-SRFE vs RFE-log-5 |
0.035 |
| 10-SRFE vs RFE-log-10 |
0.000 |
Standard deviation of number of selected features
GBM
Friedman rank sum testFriedman chi-squared = 9.0074, df = 7, p-value = 0.2521
| 3.07 |
3.88 |
3.88 |
4.38 |
4.75 |
4.79 |
5.61 |
5.66 |
Wilcoxon signed rank test
| FRFE vs RFE-log |
0.212 |
| 3-SRFE vs RFE-log-3 |
0.041 |
| 5-SRFE vs RFE-log-5 |
0.041 |
| 10-SRFE vs RFE-log-10 |
0.045 |
LR
Friedman rank sum testFriedman chi-squared = 30.955, df = 7, p-value = 6.337e-05
| 3.32 |
3.61 |
3.64 |
3.93 |
5.14 |
5.16 |
5.43 |
5.77 |
Wilcoxon signed rank test
| FRFE vs RFE-log |
0.022 |
| 3-SRFE vs RFE-log-3 |
0.005 |
| 5-SRFE vs RFE-log-5 |
0.009 |
| 10-SRFE vs RFE-log-10 |
0.000 |
RF
Friedman rank sum testFriedman chi-squared = 24.119, df = 7, p-value = 0.001086
| 3.46 |
3.88 |
4.09 |
4.11 |
4.29 |
4.64 |
5.61 |
5.93 |
Wilcoxon signed rank test
| FRFE vs RFE-log |
0.140 |
| 3-SRFE vs RFE-log-3 |
0.929 |
| 5-SRFE vs RFE-log-5 |
0.786 |
| 10-SRFE vs RFE-log-10 |
0.997 |
SVM
Friedman rank sum testFriedman chi-squared = 34.667, df = 7, p-value = 1.292e-05
| 3.18 |
3.43 |
3.75 |
4 |
5.07 |
5.39 |
5.57 |
5.61 |
Wilcoxon signed rank test
| FRFE vs RFE-log |
0.000 |
| 3-SRFE vs RFE-log-3 |
0.002 |
| 5-SRFE vs RFE-log-5 |
0.004 |
| 10-SRFE vs RFE-log-10 |
0.000 |
Accuracy
GBM
Friedman rank sum testFriedman chi-squared = 7.8272, df = 7, p-value = 0.3481
| 3.77 |
4.23 |
4.36 |
4.39 |
4.64 |
4.79 |
4.79 |
5.04 |
Wilcoxon signed rank testcannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.778 |
| 3-SRFE vs RFE-log-3 |
0.997 |
| 5-SRFE vs RFE-log-5 |
0.886 |
| 10-SRFE vs RFE-log-10 |
0.481 |
LR
Friedman rank sum testFriedman chi-squared = 3.2789, df = 7, p-value = 0.8581
| 4.11 |
4.27 |
4.3 |
4.32 |
4.55 |
4.79 |
4.82 |
4.84 |
Wilcoxon signed rank testcannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.434 |
| 3-SRFE vs RFE-log-3 |
0.623 |
| 5-SRFE vs RFE-log-5 |
0.530 |
| 10-SRFE vs RFE-log-10 |
0.841 |
RF
Friedman rank sum testFriedman chi-squared = 4.9125, df = 7, p-value = 0.6706
| 4.04 |
4.05 |
4.21 |
4.41 |
4.66 |
4.75 |
4.91 |
4.96 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.457 |
| 3-SRFE vs RFE-log-3 |
0.415 |
| 5-SRFE vs RFE-log-5 |
0.092 |
| 10-SRFE vs RFE-log-10 |
0.928 |
SVM
Friedman rank sum testFriedman chi-squared = 15.172, df = 7, p-value = 0.03385
| 3.82 |
4 |
4.02 |
4.34 |
4.45 |
4.64 |
5.14 |
5.59 |
Wilcoxon signed rank testcannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.928 |
| 3-SRFE vs RFE-log-3 |
0.847 |
| 5-SRFE vs RFE-log-5 |
0.995 |
| 10-SRFE vs RFE-log-10 |
0.839 |
Kappa
GBM
Friedman rank sum testFriedman chi-squared = 8.0687, df = 7, p-value = 0.3266
| 3.77 |
4.27 |
4.32 |
4.34 |
4.61 |
4.8 |
4.89 |
5 |
Wilcoxon signed rank testcannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.823 |
| 3-SRFE vs RFE-log-3 |
0.997 |
| 5-SRFE vs RFE-log-5 |
0.911 |
| 10-SRFE vs RFE-log-10 |
0.444 |
LR
Friedman rank sum testFriedman chi-squared = 3.1857, df = 7, p-value = 0.8673
| 4.11 |
4.29 |
4.3 |
4.38 |
4.46 |
4.82 |
4.82 |
4.82 |
Wilcoxon signed rank testcannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.536 |
| 3-SRFE vs RFE-log-3 |
0.618 |
| 5-SRFE vs RFE-log-5 |
0.518 |
| 10-SRFE vs RFE-log-10 |
0.823 |
RF
Friedman rank sum testFriedman chi-squared = 5.2849, df = 7, p-value = 0.6252
| 4.07 |
4.11 |
4.12 |
4.34 |
4.64 |
4.73 |
4.98 |
5 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.520 |
| 3-SRFE vs RFE-log-3 |
0.424 |
| 5-SRFE vs RFE-log-5 |
0.136 |
| 10-SRFE vs RFE-log-10 |
0.913 |
SVM
Friedman rank sum testFriedman chi-squared = 13.647, df = 7, p-value = 0.05784
| 3.86 |
3.95 |
4 |
4.43 |
4.52 |
4.66 |
5.07 |
5.52 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.938 |
| 3-SRFE vs RFE-log-3 |
0.835 |
| 5-SRFE vs RFE-log-5 |
0.994 |
| 10-SRFE vs RFE-log-10 |
0.855 |
Macro recall
GBM
Friedman rank sum testFriedman chi-squared = 6.6426, df = 7, p-value = 0.467
| 3.84 |
4.27 |
4.43 |
4.43 |
4.54 |
4.57 |
4.89 |
5.04 |
Wilcoxon signed rank testcannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.649 |
| 3-SRFE vs RFE-log-3 |
0.998 |
| 5-SRFE vs RFE-log-5 |
0.924 |
| 10-SRFE vs RFE-log-10 |
0.594 |
LR
Friedman rank sum testFriedman chi-squared = 4.0346, df = 7, p-value = 0.7758
| 4.18 |
4.18 |
4.29 |
4.34 |
4.39 |
4.86 |
4.88 |
4.89 |
Wilcoxon signed rank testcannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.422 |
| 3-SRFE vs RFE-log-3 |
0.540 |
| 5-SRFE vs RFE-log-5 |
0.552 |
| 10-SRFE vs RFE-log-10 |
0.726 |
RF
Friedman rank sum testFriedman chi-squared = 6.6782, df = 7, p-value = 0.4631
| 3.86 |
4.11 |
4.12 |
4.41 |
4.68 |
4.88 |
4.96 |
4.98 |
Wilcoxon signed rank testcannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.390 |
| 3-SRFE vs RFE-log-3 |
0.385 |
| 5-SRFE vs RFE-log-5 |
0.094 |
| 10-SRFE vs RFE-log-10 |
0.960 |
SVM
Friedman rank sum testFriedman chi-squared = 14.459, df = 7, p-value = 0.0436
| 3.82 |
3.98 |
4.12 |
4.27 |
4.43 |
4.71 |
5.11 |
5.55 |
Wilcoxon signed rank testcannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.930 |
| 3-SRFE vs RFE-log-3 |
0.859 |
| 5-SRFE vs RFE-log-5 |
0.994 |
| 10-SRFE vs RFE-log-10 |
0.812 |
G-mean
GBM
Friedman rank sum testFriedman chi-squared = 6.9734, df = 7, p-value = 0.4317
| 3.77 |
4.32 |
4.41 |
4.48 |
4.52 |
4.61 |
4.86 |
5.04 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.596 |
| 3-SRFE vs RFE-log-3 |
0.998 |
| 5-SRFE vs RFE-log-5 |
0.807 |
| 10-SRFE vs RFE-log-10 |
0.831 |
LR
Friedman rank sum testFriedman chi-squared = 4.5779, df = 7, p-value = 0.7113
| 4.12 |
4.27 |
4.27 |
4.29 |
4.34 |
4.84 |
4.89 |
4.98 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.177 |
| 3-SRFE vs RFE-log-3 |
0.554 |
| 5-SRFE vs RFE-log-5 |
0.470 |
| 10-SRFE vs RFE-log-10 |
0.917 |
RF
Friedman rank sum testFriedman chi-squared = 9.725, df = 7, p-value = 0.2047
| 3.71 |
4.09 |
4.21 |
4.36 |
4.59 |
4.61 |
5.07 |
5.36 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.247 |
| 3-SRFE vs RFE-log-3 |
0.251 |
| 5-SRFE vs RFE-log-5 |
0.013 |
| 10-SRFE vs RFE-log-10 |
0.987 |
SVM
Friedman rank sum testFriedman chi-squared = 12.58, df = 7, p-value = 0.08302
| 3.79 |
4.12 |
4.25 |
4.27 |
4.34 |
4.57 |
5.21 |
5.45 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with tiescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.876 |
| 3-SRFE vs RFE-log-3 |
0.610 |
| 5-SRFE vs RFE-log-5 |
0.962 |
| 10-SRFE vs RFE-log-10 |
0.762 |
Processing time
GBM
Friedman rank sum testFriedman chi-squared = 92.266, df = 7, p-value < 2.2e-16
| 2.18 |
2.5 |
4.43 |
4.71 |
4.73 |
5.09 |
5.96 |
6.39 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.500 |
| 3-SRFE vs RFE-log-3 |
0.019 |
| 5-SRFE vs RFE-log-5 |
0.040 |
| 10-SRFE vs RFE-log-10 |
0.008 |
LR
Friedman rank sum testFriedman chi-squared = 90.481, df = 7, p-value < 2.2e-16
| 1.91 |
2.41 |
4.02 |
4.91 |
5.09 |
5.23 |
5.73 |
6.7 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.524 |
| 3-SRFE vs RFE-log-3 |
0.346 |
| 5-SRFE vs RFE-log-5 |
0.232 |
| 10-SRFE vs RFE-log-10 |
0.113 |
RF
Friedman rank sum testFriedman chi-squared = 152.58, df = 7, p-value < 2.2e-16
| 1.52 |
1.84 |
3.45 |
4.66 |
4.91 |
5.55 |
6.66 |
7.41 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.978 |
| 3-SRFE vs RFE-log-3 |
1.000 |
| 5-SRFE vs RFE-log-5 |
1.000 |
| 10-SRFE vs RFE-log-10 |
0.981 |
SVM
Friedman rank sum testFriedman chi-squared = 92.818, df = 7, p-value < 2.2e-16
| 1.88 |
2.2 |
4.39 |
4.86 |
5.21 |
5.25 |
5.71 |
6.5 |
Wilcoxon signed rank testcannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroescannot compute exact p-value with zeroes
| FRFE vs RFE-log |
0.627 |
| 3-SRFE vs RFE-log-3 |
0.530 |
| 5-SRFE vs RFE-log-5 |
0.352 |
| 10-SRFE vs RFE-log-10 |
0.223 |
Friedman tests summary
prettyTable(friedman_df, 3:ncol(friedman_df), 3)
Feature selection plots
df_grid <- read.csv("GridScores.csv", na.strings = c("?", "", "-")) %>%
mutate(Dataset = as.character(Dataset), Classifier = as.character(Classifier))
df_grid$Dataset <- substr(df_grid$Dataset, 1, nchar(df_grid$Dataset)-4)
df_grid$Dataset <- as.factor(df_grid$Dataset)
df_grid$Classifier[startsWith(df_grid$Classifier, "SVC")] <- "SVM"
df_grid$Classifier[startsWith(df_grid$Classifier, "Random")] <- "RF"
df_grid$Classifier[startsWith(df_grid$Classifier, "Logistic")] <- "LR"
df_grid$Classifier[startsWith(df_grid$Classifier, "LGBM")] <- "GBM"
df_grid$Classifier <- as.factor(df_grid$Classifier)
df_grid$Feature.selector = factor(df_grid$Feature.selector,levels(df_grid$Feature.selector)[c(4,2,3,1,5,7,8,6)])
df_grid$Type <- as.character(df_grid$Feature.selector)
df_grid$Type[df_grid$Type == "FRFE" | df_grid$Type == "3-SRFE" | df_grid$Type == "5-SRFE" | df_grid$Type == "10-SRFE"] <- "FRFE/k-SRFE"
df_grid$Type[df_grid$Type != "FRFE/k-SRFE"] <- "RFE"
df_grid$Type <- as.factor(df_grid$Type)
cbPalette <- c("#a6cee3", "#b2df8a", "#fb9a99", "#fdbf6f", "#1f78b4", "#33a02c", "#e31a1c", "#ff7f00")
for (dataset in unique(df_grid$Dataset)){
plot_df <- df_grid %>% filter(Dataset == dataset, Fold == 0)
p <- ggplot(plot_df, aes(Feature.num, Accuracy, color=Feature.selector)) + geom_point(alpha=0.6) + facet_grid(Classifier~Type) + theme_bw() + scale_color_manual(values=cbPalette, name="Selector") + xlab("Number of features")
ggsave(paste0("images/", dataset, ".svg"), plot = p, dpi = 300, width=8, height=4, units = "in")
ggsave(paste0("images/", dataset, ".png"), plot = p, dpi = 300, width=8, height=4, units = "in")
print(p + ggtitle(dataset))
}



























---
title: "Fibonacci and k-Subsecting Recursive Feature Elimination: Supplement"
output: 
  html_notebook: 
    code_folding: hide
    number_sections: yes
    toc: yes
    toc_depth: 2
---

```{r libraries, message=FALSE, warning=FALSE}
library(knitr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(plotly)
library(DT)
library(ggthemes)
library(PMCMR)
library(scmamp)

knitr::opts_chunk$set(dpi=96, results = "asis")
knitr::knit_hooks$set(inline = function(x) {
  prettyNum(x, big.mark=",")
})
SAVE_EPS = F
```

```{r functions}
prettyTable <- function(table_df, round_columns=numeric(), round_digits=3, make_plot=T) {
    DT::datatable(table_df, style="bootstrap", filter = "top", rownames = FALSE, extensions = "Buttons",
                  options = list(dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel', 'pdf', 'print'))) %>%
    formatRound(round_columns, round_digits)
}

reportFriedman <- function(df, classifier, metric, metric_function, metric_direction=1, make_plot = T){
    averages = data.frame(df) %>%
        select(-c(Attributes, Number.of.classes, Min.class.examples, Max.class.examples)) %>%
        group_by(Dataset, Classifier, Feature.selector) %>%
        summarise_all(metric_function, na.rm=T) %>%
        data.frame() %>%
        filter(Feature.selector != 'All', Classifier == classifier) %>%
        select_("Dataset", "Feature.selector", metric) %>%
        spread("Feature.selector", metric) %>%
        select(-Dataset) %>%
        select(`FRFE`, `RFE-log`, `3-SRFE`, `RFE-log-3`, `5-SRFE`, `RFE-log-5`, `10-SRFE`, `RFE-log-10`) %>%
        data.matrix()
    
    if (metric_direction == 1) {
      averages_r = -averages
      averages_t = averages
    } else {
      averages_r = averages
      averages_t = -averages
    }
    ranks <- t(apply(averages_r, 1, rank))
    ranks <- ranks[,order(colMeans(ranks, na.rm=TRUE))]

    cat("<hr><strong>Friedman rank sum test</strong><br />")
    fTest <- friedman.test(averages_t)
    testResult <- capture.output(print(fTest))
    cat(testResult[5])
    cat("\r\n")
    mean_ranks <- t(colMeans(ranks, na.rm=TRUE))
    print(kable(mean_ranks, digits = 2))
    cat("\r\n")
    
    w_df <- data.frame(Subsecting = c("FRFE", "3-SRFE", "5-SRFE", "10-SRFE"),
                       Standard = c("RFE-log", "RFE-log-3", "RFE-log-5", "RFE-log-10"), 
                       p.value = c(-1, -1,-1, -1)) %>%
      mutate(Hypothesis = paste0(Subsecting, " vs ", Standard))
    cat("<hr><strong>Wilcoxon signed rank test</strong><br />")
    for (r in 1:nrow(w_df)){
      wTest <- wilcox.test(averages_t[,as.character(w_df[r, "Subsecting"])],
                           averages_t[,as.character(w_df[r, "Standard"])],
                           paired=TRUE, alternative = "g")
      w_df[r, "p.value"] <- wTest$p.value
    }
    print(kable(w_df %>% select(Hypothesis, p.value), digits = 3))
    cat("\r\n")
    
    
    
    if (make_plot){
        setEPS()
        par(mar=c(0,0,0,0))
        postscript(paste0("images/", metric, "_", classifier, "_",  "Friedman.eps"), width = 7, height = 3.4)
        plotCD(results.matrix = averages_t, alpha = 0.05, cex = 1.1)
        dev.off()
    }

    cat("<hr>")
    mean_ranks_df <- data.frame(`FRFE` = mean_ranks[1, "FRFE"], `RFE-log` = mean_ranks[1, "RFE-log"], `3-SRFE` = mean_ranks[1, "3-SRFE"], `RFE-log-3` = mean_ranks[1, "RFE-log-3"], `5-SRFE` = mean_ranks[1, "5-SRFE"], `RFE-log-5` = mean_ranks[1, "RFE-log-5"], `10-SRFE` = mean_ranks[1, "10-SRFE"], `RFE-log-10` = mean_ranks[1, "RFE-log-10"], `p-value` = fTest$p.value)
    mean_ranks_df
}
```


# Results summary

## Raw results

```{r raw summary}
df <- read.csv("Benchmarks.csv", na.strings = c("?", "", "-")) %>%
    mutate(Dataset = as.character(Dataset), Classifier = as.character(Classifier), Selected.num = as.character(Selected.num)) %>%
    select(-c(Start.date, Selector.params, Scorer, Grid.scores, Selected.features))


df$Dataset <- substr(df$Dataset, 1, nchar(df$Dataset)-4)
df$Dataset <- as.factor(df$Dataset)

df$Classifier[startsWith(df$Classifier, "SVC")] <- "SVM"
df$Classifier[startsWith(df$Classifier, "Random")] <- "RF"
df$Classifier[startsWith(df$Classifier, "Logistic")] <- "LR"
df$Classifier[startsWith(df$Classifier, "LGBM")] <- "GBM"
df$Classifier <- as.factor(df$Classifier)

df$Selected.num[df$Selected.num == "error"] <- NA
df$Selected.num <- as.numeric(df$Selected.num)

prettyTable(df, c(9, 11:15))
```

## Datasets

```{r dataset summary}
datasets_df <- df %>%
  select(Dataset, Examples, Attributes, Number.of.classes, Min.class.examples, Max.class.examples) %>%
  distinct()
  
prettyTable(datasets_df)
```

## Mean cross-validation scores

```{r cv summary, warning=F}
cv_df <- df %>%
  select(-c(Attributes, Number.of.classes, Min.class.examples, Max.class.examples)) %>%
  group_by(Dataset, Classifier, Feature.selector) %>%
  summarise_all(mean, na_rm=T)
  

prettyTable(cv_df, c(5, 8:12))
```

# Comparisons and statistical tests {.tabset}

```{r, results="asis"}
classifiers <- as.character(unique(cv_df$Classifier))
metrics <- c("Selected.num", "Selected.num", "Accuracy", "Kappa", "Macro.recall", "G.mean", "Processing.time")
metric_functions <- c(mean, sd, mean, mean, mean, mean, mean)
mertric_headers <-c("Number of selected features", "Standard deviation of number of selected features", "Accuracy", "Kappa", "Macro recall", "G-mean", "Processing time")
metric_direction <- c(-1, -1, 1, 1, 1, 1, -1)

friedman_df <- data.frame(Metric = character(), Classifier = character(), `FRFE` = numeric(), `RFE-log` = numeric(), `3-SRFE` = numeric(), `RFE-log-3` = numeric(), `5-SRFE` = numeric(), `RFE-log-5` = numeric(), `10-SRFE` = numeric(), `RFE-log-10` = numeric(), `p-value` = numeric())

for (i in seq_along(metrics)){
  cat(paste0("## ", mertric_headers[i],"\r\n\r\n"))
  for (classifier in classifiers){
    cat(paste0("### ", classifier,"\r\n\r\n"))
    mean_ranks <- reportFriedman(df, classifier, metrics[i], metric_functions[i], metric_direction[i], make_plot = F)
    mean_df <- data.frame(Metric = mertric_headers[i], Classifier = classifier, `FRFE` = mean_ranks$FRFE, `RFE-log` = mean_ranks$RFE.log, `3-SRFE` = mean_ranks$X3.SRFE, `RFE-log-3` = mean_ranks$RFE.log.3, `5-SRFE` = mean_ranks$X5.SRFE, `RFE-log-5` = mean_ranks$RFE.log.5, `10-SRFE` = mean_ranks$X10.SRFE, `RFE-log-10` = mean_ranks$RFE.log.10, `p-value` = mean_ranks$p.value)
    friedman_df <- rbind(friedman_df, mean_df)
  }
}

```

## Friedman tests summary

```{r freidmanSummary}
prettyTable(friedman_df, 3:ncol(friedman_df), 3)
```


# Feature selection plots

```{r feature plots}
df_grid <- read.csv("GridScores.csv", na.strings = c("?", "", "-")) %>% 
  mutate(Dataset = as.character(Dataset), Classifier = as.character(Classifier))
df_grid$Dataset <- substr(df_grid$Dataset, 1, nchar(df_grid$Dataset)-4)
df_grid$Dataset <- as.factor(df_grid$Dataset)

df_grid$Classifier[startsWith(df_grid$Classifier, "SVC")] <- "SVM"
df_grid$Classifier[startsWith(df_grid$Classifier, "Random")] <- "RF"
df_grid$Classifier[startsWith(df_grid$Classifier, "Logistic")] <- "LR"
df_grid$Classifier[startsWith(df_grid$Classifier, "LGBM")] <- "GBM"
df_grid$Classifier <- as.factor(df_grid$Classifier)

df_grid$Feature.selector = factor(df_grid$Feature.selector,levels(df_grid$Feature.selector)[c(4,2,3,1,5,7,8,6)])

df_grid$Type <- as.character(df_grid$Feature.selector)
df_grid$Type[df_grid$Type == "FRFE" | df_grid$Type == "3-SRFE" | df_grid$Type == "5-SRFE" | df_grid$Type == "10-SRFE"] <- "FRFE/k-SRFE"
df_grid$Type[df_grid$Type != "FRFE/k-SRFE"] <- "RFE"
df_grid$Type <- as.factor(df_grid$Type)


cbPalette <- c("#a6cee3", "#b2df8a", "#fb9a99", "#fdbf6f", "#1f78b4", "#33a02c", "#e31a1c", "#ff7f00")

for (dataset in unique(df_grid$Dataset)){
  plot_df <- df_grid %>% filter(Dataset == dataset, Fold == 0)
  p <- ggplot(plot_df, aes(Feature.num, Accuracy, color=Feature.selector)) + geom_point(alpha=0.6) + facet_grid(Classifier~Type) + theme_bw() + scale_color_manual(values=cbPalette, name="Selector") + xlab("Number of features")
  ggsave(paste0("images/", dataset, ".svg"), plot = p, dpi = 300, width=8, height=4, units = "in")
  ggsave(paste0("images/", dataset, ".png"), plot = p, dpi = 300, width=8, height=4, units = "in")
  print(p + ggtitle(dataset))
}
```





